NAACL 2024long0 citations
XferBench: a Data-Driven Benchmark for Emergent Language
Brendon Boldt, David Mortensen
Abstract
In this paper, we introduce a benchmark for evaluating the overall quality of emergent languages using data-driven methods. Specifically, we interpret the notion of the “quality” of an emergent language as its similarity to human language within a deep learning framework. We measure this by using the emergent language as pretraining data for a downstream NLP tasks in human language—the better the downstream performance, the better the emergent language. We implement this benchmark as an easy-to-use Python package that only requires a text file of utterances from the emergent language to be evaluated. Finally, we empirically test the benchmark’s validity using human, synthetic, and emergent language baselines.
BibTeX
@inproceedings{boldt-mortensen-2024-xferbench,
title = "{X}fer{B}ench: a Data-Driven Benchmark for Emergent Language",
author = "Boldt, Brendon and
Mortensen, David",
editor = "Duh, Kevin and
Gomez, Helena and
Bethard, Steven",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.naacl-long.82/",
doi = "10.18653/v1/2024.naacl-long.82",
pages = "1475--1489"
}